Modern Statistical Prediction and Machine Learning
Instructor: Nusrat Rabbee, email@example.com.
Oﬃce: 309 Evans.
Oﬃce hours: T 4-5:00pm, or after class or by appointment in class
Graduate Student Instructor: Omid Shams Solari, firstname.lastname@example.org.
Office hours TBA
Website: http://www.stat.berkeley.edu/users/rabbee/s154. We will post announcements, assignments, lecture notes etc. on bcourses.berkeley.edu. Check regularly for updates.
Schedule: There will be lectures two days a week, TTh 5:00-6:30, in Moffitt Library 145. I don’t allow cell phones, iPads or laptops in lecture. If you want to use a device to take notes – please speak to me. There will also be weekly sections, scheduled F 9-11p or 1-3p, starting 9/2. Attendance to both lectures and sections is highly encouraged.
2. Optional: Hastie, Tibshirani and Friedman, The Elements of Statistical Learning. Second Edition. This book is more mathematically advanced than the one above. Hardcopy. Online version. (Courtesy of the authors). This text will not be used directly for this course and is simply a reference for more theoretical details.
Exams and grading: There will be one written exam (Th 10/13 during class) and a ﬁnal project (due F 12/2 noon). There will be five to six quizzes during section. There will be no make-up quiz, written exam or ﬁnal project due date adjustments; do not take the class if you are not available at these dates and times. Your grade will be 50% best five quizzes, 20% written exam, 30% ﬁnal project.
Assignments: There will be six to seven assignments. They are announced in bCourses on Fridays. The assignments are not to be handed in. You should do the assignments in teams or by yourself. The quizzes will be similar to the assignment sets.
Academic Integrity: You may collaborate with your assigned team for the final project and you will share the same score. No collaboration is allowed in the quizzes or exams. Penalties for cheating will be severe. Here are more details. Special instructions for final project teams will be announced later.
Communicating: Questions about lectures should be directed primarily to me after lecture or in office hours, about section and assignments primarily to the GSI. Emails are generally discouraged. Write to me only if you have any pressing administrative issues. Emails should be brief, marked “stat 154” in the subject and crisp for a good chance at being answered. Regardless, you are encouraged to come to any of our oﬃce hours or stay after class: talking is usually more effective than sending email. Feedback is always welcome.
Prerequisites: Mathematics 53 and 54 or equivalents; 110 is highly recommended. Statistics 135 or equivalent. Statistics 133 preferred. Stat 151A is recommended. Scripting language and R experience required. Mathematics 55 or equivalent exposure to counting arguments is recommended but not required.